Extracting card data with card models

ABSTRACT

Embodiments herein provide computer-implemented techniques for allowing a user computing device to extract financial card information using optical character recognition (“OCR”). Extracting financial card information may be improved by applying various classifiers and other transformations to the image data. For example, applying a linear classifier to the image to determine digit locations before applying the OCR algorithm allows the user computing device to use less processing capacity to extract accurate card data. The OCR application may train a classifier to use the wear patterns of a card to improve OCR algorithm performance. The OCR application may apply a linear classifier and then a nonlinear classifier to improve the performance and the accuracy of the OCR algorithm. The OCR application uses the known digit patterns used by typical credit and debit cards to improve the accuracy of the OCR algorithm.

RELATED APPLICATIONS

This patent application is a continuation of and claims priority to U.S.patent application Ser. No. 14/645,410, filed Mar. 11, 2015, andentitled “Extracting Card Data With Card Models,” which is acontinuation of and claims priority to U.S. patent application Ser. No.14/461,001, filed Aug. 15, 2014, and entitled “Extracting Card Data WithCard Models,” which is a continuation of and claims priority to U.S.patent application Ser. No. 14,059,151 filed Oct. 21, 2013, and entitled“Extracting Card Data With Card Models,” which claims priority under 35U.S.C. §119 to U.S. Provisional Patent Application No. 61/841,268 filedJun. 28, 2013, and entitled “Hierarchical Classification in Credit CardData Extraction.” The entire contents of the above-identified priorityapplications are hereby fully incorporated herein by reference.

TECHNICAL FIELD

The technology disclosed herein pertains to extracting financial cardinformation, and more particularly to using classifiers to improveaccuracy and reduce required processor capacity.

BACKGROUND

When a consumer makes an online purchase or a purchase using a mobiledevice, they are often forced to enter credit card information into themobile device for payment. Due to the small screen size and keyboardinterface on a mobile device, such entry is generally cumbersome andprone to errors. Users may use many different cards for purchases, suchas credit cards, debit cards, stored value cards, and other cards.Information entry difficulties are multiplied for a merchant attemptingto process mobile payments on mobile devices for multiple transactions.

Current applications for obtaining payment information or other carddata from a payment card requires a certain amount of processingcapacity from the user computing device. Many current methods forextracting the card data from the image require an amount of processingcapacity that is greater than the user computing device can easilyprovide. The strain on the processing capacity may cause delays, errors,battery drain, and other processor problems.

Current applications do not allow various classifications, models, andmachine learning algorithms to be used for more accurate results.

SUMMARY

The technology of the present disclosure includes computer-implementedmethods, computer program products, and systems for extracting cardinformation. Extracting card information comprises receiving, by one ormore computing devices, an image of a card from a camera; identifying afirst area of the image, the first area being selected as a potentiallocation of a digit on the card in the image; performing a linearclassification algorithm on data encompassed by the first area;determining a confidence level of a first result of the application ofthe linear classification algorithm to the first area; determining thatthe first area encompasses a digit upon determining that the confidencelevel of the first result is over a configured threshold; and performingan optical character recognition algorithm on the first area upon adetermination that the first area encompasses the digit. Another examplemay train a classifier to use the wear patterns of a card to improveoptical character recognition (“OCR”) algorithm performance. Anotherexample may apply a linear classifier and then a nonlinear classifier toimprove the performance and the accuracy of the OCR algorithm. Anotherexample uses the known digit patterns used by typical credit and debitcards to improve the accuracy of the OCR algorithm.

These and other aspects, objects, features, and advantages of theexample embodiments will become apparent to those having ordinary skillin the art upon consideration of the following detailed description ofillustrated example embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram depicting a system for extracting accountinformation from a card, in accordance with certain example embodimentsof the technology disclosed herein.

FIG. 2 is a block flow diagram depicting methods for extracting accountinformation using a linear classifier, in accordance with certainexample embodiments.

FIG. 3 is a block flow diagram depicting methods for applying a linearclassifier to locate digits, in accordance with certain exampleembodiments.

FIG. 4 is a block flow diagram depicting methods for an OCR system tomodify a linear classier to identify wear patterns, in accordance withcertain example embodiments.

FIG. 5 is a block flow diagram depicting methods for extracting accountinformation using wear patterns, in accordance with certain exampleembodiments.

FIG. 6 is a block flow diagram depicting methods for extracting accountinformation using linear and non-linear classifiers, in accordance withcertain example embodiments.

FIG. 7 is a block flow diagram depicting methods for extracting accountinformation using card models, in accordance with certain exampleembodiments.

FIG. 8 is an illustration of a user computing device displaying an imageof a financial card, in accordance with certain example embodiments

FIG. 9 is a block diagram depicting a computing machine and a module, inaccordance with certain example embodiments.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Overview

Embodiments herein provide computer-implemented techniques for allowinga user computing device to extract financial card information usingoptical character recognition (“OCR”). The process of extractingfinancial card information may be improved by applying variousclassifiers and other transformations to the image data. For example,applying a linear classifier to the image to determine digit locationsbefore applying the OCR algorithm allows the user computing device touse less processing capacity to extract accurate card data. The OCRapplication may compare typical wear patterns to train a classifier. TheOCR application may use the wear patterns of a user to improve OCRalgorithm performance. The OCR application may apply a lineartransformation and then a nonlinear transformation to improve theperformance and the accuracy of the OCR algorithm. The lineartransformation may reduce the dimensionality of the data to reduceprocessing requirements, and the nonlinear transformation may improvethe accuracy of the resulting data. The OCR application may use cardmodels to improve the accuracy of the OCR algorithm. For example, theOCR application may use the known digit patterns used by typical creditand debit cards to improve the accuracy of the OCR algorithm.

Throughout the specification, the general term “card” will be used torepresent any type of physical card instrument, such as a magneticstripe card. In example embodiments, the different types of cardrepresented by “card” can include credit cards, debit cards, storedvalue cards, loyalty cards, identification cards, or any other suitablecard representing an account or other record of a user or otherinformation thereon. Example embodiments described herein may be appliedto the images of other items, such as receipts, boarding passes,tickets, and other suitable items. The card may also be an image orfacsimile of the card. For example, the card may be a representation ofa card on a display screen or a printed image of a card.

The user may employ the card when making a transaction, such as apurchase, ticketed entry, loyalty check-in, or other suitabletransaction. The user may obtain the card information for the purpose ofimporting the account represented by the card into a digital walletapplication module or for other digital account purposes. The card istypically a plastic card containing the account information and otherdata on the card. In many card embodiments, the customer name,expiration date, and card numbers are physically embossed on the card.The embossed information is visible from both the front and back of thecard, although the embossed information is typically reversed on theback of the card.

A user may desire to enter the information from the card into a mobileuser computing device or other computing device, for example, to conductan online purchase, to conduct a purchase at a merchant location, to addthe information to a wallet application on a user computing device, orfor any other suitable reason. In an example, the user desires to use amobile user computing device to conduct a purchase transaction using adigital wallet application module executing on the mobile user computingdevice. The digital wallet application module may require an input ofthe details of a particular user payment account to conduct atransaction with the particular user payment account or to set up theaccount. Due to the small screen size and keyboard interface on a mobiledevice, such entry can be cumbersome and error prone. Additionally, amerchant system may need to capture card information to conduct atransaction or for other reasons.

In addition to account identifiers, the front of the card typicallycontains logos of the issuer of the card, pictures chosen by the user orthe issuer, other text describing the type or status of the useraccount, a security code, and other marketing and security elements,such as holograms or badges. The user name, card expiration date, andthe account identifier, such as a credit card number, may be embossed onthe front of the card such that the information protrudes from the frontof the card.

The user employs a mobile phone, digital camera, or other user computingdevice to capture a scan of the card associated with the account thatthe user desires to input into the user computing device.

An OCR application on the user computing device receives a scan of thecard. The scan, or digital scan, may be a video of the card, a series ofimages of the card, or data from any other suitable scanning technology.The image may be obtained from the camera module of a user computingdevice, such as the camera on a mobile phone. The images may be obtainedfrom any digital image device coupled to the user computing device orany other suitable digital imaging device. The images may be accessed bythe OCR application on the user computing device from a storage locationon the user storage device, from a remote storage location, or from anysuitable location. All sources capable of providing the image will bereferred to as a “camera.”

An OCR application receives the images of the card from the camera. Thefunctions of the OCR application may be performed by any suitablemodule, hardware, software, or application operating on the usercomputing device. Some, or all, of the functions of the OCR applicationmay be performed by a remote server or other computing device, such asthe server operating in an OCR system. For example, a digital walletapplication module on the user computing device may obtain the images ofthe card and transmit the images to the OCR system for processing. Inanother example, some of the OCR functions may be conducted by the usercomputing device and some by the OCR system or another remote server.Examples provided herein may indicate that many of the functions areperformed by an OCR application on the user computing device, but someor all of the functions may be performed by any suitable computingdevice.

The image of the card is presented on the user interface of the usercomputing device as a live video image of the card. The OCR applicationcan isolate and store one or more images from the video feed of thecamera. The OCR application may store a scan of the card as a video orother suitable format comprising multiple images of the card. Forexample, the user may hover the camera function of a user computingdevice over a financial card and observe the representation of thefinancial card on the user interface of the user computing device. Theuser may actuate a real or virtual button on the user computing deviceto capture a preferred image or group of images. The OCR application mayselect the preferred images automatically.

In certain examples, some or all of the functions described areperformed while the scan is active. For example, the user may hover thecamera of a user computing device over the card and the methodsdescribed herein are performed with live images of the card. That is,the OCR application captures and utilizes images from the active feed ofthe camera.

The OCR application, the camera module, or the user computing device, orother computing device performs blur detection on the images. The imagemay be recognized as blurry, overly bright, overly dark, or otherwiseobscured in a manner that prevents a high resolution image from beingobtained. The OCR application, or other function of the user computingdevice or the camera, may adjust the image capturing method to reducethe blur in the image. For example, the OCR application may direct thecamera to adjust the focus on the financial card. In another example,the OCR application may direct the user to move the camera closer to, orfarther away from, the financial card. In another example, the OCRapplication may perform a digital image manipulation to remove the blur.Any other method of correcting a blurred image can be utilized.

The OCR application may crop the images to display only the desiredinformation from the card. In an example, if the card in the image is acredit card, the OCR application accesses information associated withthe expected location of the account number of a credit card. Theexpected location may be obtained from a database of card layouts storedon the user computing device or in another suitable location. Forexample, credit cards, driver's licenses, loyalty cards, and other cardstypically meet an industry or issuer standard for the data locations andthe layout of the card. The standards may be stored in the OCRapplication or in a location accessible by the OCR application.

The OCR application isolates a small window on the card. The window maybe located in an area where a digit is expected to be located. Thewindow may be slightly larger than the expected digit to allow an entiredigit to be encompassed by the window, but not so large as to encompassmore than one digit or more than a limited number of digits. Otherwindows may be utilized that encompass multiple digits.

The OCR application applies a linear classifier or other suitableclassifier to the window. The OCR application may use a simple linearsupport vector machine (“SVM”) to distinguish a digit from thebackground. An SVM is a machine learning tool that may utilizealgorithms to analyze data and recognize patterns. The SVM linearclassifier the OCR application applies to the window may use lessprocessing capacity from the user computing device than a more typicalOCR algorithm. Any suitable machine learning tool may be used todistinguish the digits in addition to, or in place of, the SVM.

The classifier determines a confidence level that the image in thewindow is a digit. For example, if the window completely encompasses asingle digit, the classifier may determine that the image is likely tobe a digit and provide a high confidence level. The OCR application, oranother suitable party, may configure a threshold value for determiningif the window encompasses a digit. Alternatively, one can sort theclassification scores over all the windows and pick a configured numberof the top scoring windows as desired.

If the results of the classifier determine that the confidence level isunder a threshold, then the OCR application shifts and/or resizes thewindow position on the image. The OCR application may shift the windowlocation by as little as a pixel on the image or by any other distance.The OCR application may shift the window horizontally, vertically, ordiagonally. The OCR application may alter the size of the window. Anysuitable revision to the window may be performed.

The OCR application applies the linear classifier to the new window. TheOCR application may shift the window and run linear classifiers untilall the locations of all digits are identified and the confidence levelswithin those locations are all over the threshold. Additionally oralternatively, all possible locations of the image may be analyzed.

If the results of the linear classifier indicate that the confidencelevel is over a threshold, then the OCR application applies an OCRalgorithm to the digits in the selected window locations. The OCRapplication may apply the OCR algorithm to all of the windows or digitsthat are over the confidence level, to an individual window or digit, orto any portion of the card image.

The OCR application supplies the extracted data to a requestor, such asa digital wallet application module, point of sale terminal, paymentprocessing system, website, or any suitable application or system thatthe user desires. The extracted data may be used by an application onthe user computing device. The extracted data may be transmitted via anInternet connection over the network, via a near field communication(“NFC”) technology, emailed, texted, or transmitted in any suitablemanner.

In another example embodiment, the OCR system trains an SVM classifierto identify wear patterns on cards. Embossed digits, printed digits, andother digits on a card often become distressed over a period time. Onesource of distress is wear from being inserted into and out of a wallet,purse, pocket, or other enclosure. The digits can develop a wear patternfrom being forced into an enclosure repeatedly. For example, if a userforces a credit card into a slot in a wallet from the top down, the cardcan develop a wear pattern from the friction of the card on the walletmaterial.

The OCR system or any other suitable system may train a classifier toidentify the wear patterns. The classifier may be trained by receivingdata from an operator or another suitable party. In an example, theclassifier analyzes images of cards. The images may be taken from actualcard scans, generated by a computer, designed by an operator, taken froma demonstration group, or obtained from any suitable source. Theclassifier may identify digits in a manner as described herein or in anysuitable manner.

The OCR system operator, a user, or another suitable party may providecorrections for incorrectly classified digits. The operator may view theresults of a classification on a user interface of the OCR system andcorrect classification errors. For example, a window may be classifiedas not containing a digit when a digit was completely contained in thewindow but worn away sufficiently that the OCR system does not detectthe digit. The operator instructs the classifier that the window didcontain a digit. The classifier learns that the characteristicsindicated by the classification of the window are to be incorporated infuture classifications. The classifier learns from the corrections tomore accurately interpret the image data. For example, a particular wearpattern may wear off a bottom half of a digit. The OCR system willinterpret a window encompassing the digit as not containing an entiredigit, because the bottom half of the digit is missing. However, the tophalf of the supplied digit can be used to interpret the whole digit, andthe classifier can be instructed to recognize the whole digit based onthe top half of the supplied digit. Additionally, the classifier can beinstructed to identify the top half of digits in other windows (or whenanalyzing other cards) and to supply the missing bottom half of thedigit based on the applied wear pattern.

In an example, the classifier learns to interpret data from the imagesof cards that have distressed digits. For example, the digits may have awear pattern that was created by sliding the card into a holder. Theclassifier may be instructed to recognize the wear patterns. Theclassifier may further be instructed to categorize the wear patterns.For example, a wear pattern may be associated with a category such as“top down wear.” top down wear might be indicated by a wear pattern thatis created when a card is repeatedly inserted into a holder in thatmanner. Other wear pattern categories might include “left sidehorizontal wear,” “diagonal wear,” “bottom up wear,” or in any suitablecategory.

The OCR system may create transformations to apply to classificationdata. For example, the OCR system may learn what optical effect eachcategory of wear pattern creates in the card image and, in particular,to specific digits. The OCR system may create a transformation to applyto classification data that allows the classification data to berectified, or otherwise revised, to a standard digit image.

The OCR application employs the learned algorithm for the classifier.The OCR application may use a linear classifier on a card image asdescribed herein and determine the confidence level of the result.

The OCR application applies a transformation to the linearclassification of the data, such as a Top Down Wear transformation. Forexample, in a “top down wear” transformation, the OCR application willidentify the bottom portion of a number (in other words, a numbermissing at least a portion of the top of the number). Then, using thebottom portion of the number, the OCR application can identify theentire number. The OCR application may determine if the confidence levelof the result improves after the top down wear transformation. If theconfidence level improves, then the OCR application determines that thecard in the image has a wear pattern associated with the top down wearpattern.

The OCR application may apply any or all of the wear patterntransformations and determine the confidence level of each result. TheOCR application selects the top performing result. The OCR applicationmay additionally or alternatively combine wear pattern transformationsin any combination that provide a suitable result. For example, if auser randomly places card in his card holder, then that user's cards mayexhibit multiple wear patterns.

After determining the wear pattern associated with the card of the user,the OCR application applies the wear pattern to all future card images.That is, when the user applies a classification and an OCR algorithm tofuture card images, the OCR application applies the determined wearpattern to the classification. The OCR application may assume that theuser creates a similar wear pattern on all cards utilized by the user.For example, the user may store all cards in a similar manner in awallet or other card holder. Thus, a similar wear pattern may be createdon all cards of the user. The OCR application uses the predicted wearpattern to improve the accuracy and the speed of the OCR application.

In another aspect, the OCR application may apply linear and nonlineartransformation methods to the card images. The OCR application may applythe methods to the transformations or to the features of the image.Linear transformation methods may include techniques such as simplelinear support vector machines (“SVM”), principal components analysis(“PCA”), and linear discriminant analysis (“LDA”). An example ofnonlinear classification methods includes Random Fourier Feature Mapping(“RFFM”).

The OCR application generates more accurate results with less processingrequirements by combining linear and nonlinear transformation methods.The OCR application applies a linear transformation to the image data toreduce the dimensionality and then applies the nonlinear transformationto increase the accuracy of the results. Dimensionality reduction is theprocess of reducing the number of random variables under consideration.The main linear technique for dimensionality reduction, principalcomponent analysis, performs a linear mapping of the data to a lowerdimensional space in such a way that the variance of the data in thelow-dimensional representation is maximized. The covariance matrix ofthe data is constructed and the eigenvectors on this matrix arecomputed. The eigenvectors that correspond to the largest eigenvalues(the principal components) can now be used to reconstruct a largefraction of the variance of the original data. The original space (withdimension of the number of points) has been reduced (with data loss, buthopefully retaining the most important variance) to the space spanned bya few eigenvectors.

In the example, the OCR application obtains a card image as describedherein. The OCR application applies a linear transformation as describedherein. The linear transformation method may be selected to reduce thedimensionality of the input features while minimizing the impact on theaccuracy of the results. The OCR application may additionally oralternatively select a method that requires the least processingcapacity from the user network device.

The OCR application obtains the results of the linear transformation andapplies a nonlinear transformation method to the results. The nonlineartransformation method improves the accuracy of the data. By firstreducing the dimensionality of the data with the linear transformationmethods and applying the nonlinear transformation methods, the OCRapplication achieves higher accuracy with reduced processing costs.

After applying the transformation methods and applying the OCR algorithmas described herein, the OCR application applies a classificationapplication to the results. Then, the OCR application supplies therelevant card information to a requestor as described herein.

In another example embodiment, the OCR application identifies lines ofdigits on an image and uses card models to predict digit locations on acard image. The OCR application obtains an image of a card as describedherein. The OCR application applies a linear classifier to the image asdescribed herein. The OCR application determines the potential digitlocations. That is, the OCR application can score each potential digitwindow and select the windows most likely to contain a digit.

The OCR application identifies lines on the card image by fitting linesto the likely digits. That is, the OCR application may apply lines tothe image that are fitted to the top or bottom of a series of digits. Anexample technique for robust detection of lines in presence of noisywindow selection is Random Sample Consensus (“RANSAC”).

For example, the digits comprising the user identification number of acredit card are typically printed or embossed in a line from left toright across the face of the card. The OCR application recognizes thatthe potential digits are arranged in a line or a series of lines. TheOCR application may store the lines that are most likely to be lines ofdigits. For example, the OCR application may store the top scoring 3 or4 lines.

The OCR application maintains a series of card models in a database orother storage location. The card models indicate the manner in which thedigits are displayed on the card. For example, certain card issuersdisplay the account numbers in a particular spacing configuration and ata certain location on the card. A certain card issuer may display thedigits of the account number in a continuous string of digits withoutspaces. Another card issuer may display the digits of the account numberin groups of 4 digits divided by spaces. Another card issuer may displaythe digits of the account number in groups of 4 digits with a doublespace in the center of the digits. Any suitable digit grouping may beidentified and stored in the database.

The OCR application fits the card models to the image. The OCRapplication may fit the model along the lines stored from the image.That is, the OCR application fits the model along each of the lines thatwere determined to be likely digit location lines. In another example,the model is fit to every location on the card image. The model may beapplied in a location against the left edge of the card and then shiftedone pixel at a time horizontally or vertically to find the best fit. Anyother method of fitting the model to the image may be employed.

The OCR application applies an OCR algorithm to the potential digitlocations determined by the lines, the windows, and the models. The OCRapplication determines the best digit recognition scores from the OCRalgorithm applications.

The OCR application determines the model that creates the best digitrecognition scores. For example, a model that has the digits of theaccount number in groups of 4 digits may generate the best results. TheOCR application may predict a credit card issuer that is associated withthe account number groupings. The OCR application uses the knowledge ofthe credit card issuer to predict other data locations on the image orfor any suitable validation, prediction, or verification purposes.

The OCR application selects the best digits located by the lines, thewindows, and the models. The OCR application may select the best digitsand supply the relevant card information to a requestor.

Example System Architecture

Turning now to the drawings, in which like numerals represent like (butnot necessarily identical) elements throughout the figures, exampleembodiments are described in detail.

FIG. 1 is a block diagram depicting a system for extracting financialaccount information with relaxed card alignment and for extractingfinancial account information from multiple cards, in accordance withcertain example embodiments. As depicted in FIG. 1, the system 100includes network computing devices 110, 120, 140, and 170 that areconfigured to communicate with one another via one or more networks 105.In some embodiments, a user associated with a device must install anapplication and/or make a feature selection to obtain the benefits ofthe techniques described herein.

Each network 105 includes a wired or wireless telecommunication means bywhich network devices (including devices 110, 120, 140, and 170) canexchange data. For example, each network 105 can include a local areanetwork (“LAN”), a wide area network (“WAN”), an intranet, an Internet,a mobile telephone network, or any combination thereof. Throughout thediscussion of example embodiments, it should be understood that theterms “data” and “information” are used interchangeably herein to referto text, images, audio, video, or any other form of information that canexist in a computer-based environment.

Each network computing device 110, 120, 140, and 170 includes a devicehaving a communication module capable of transmitting and receiving dataover the network 105. For example, each network device 110, 120, 140,and 170 can include a server, desktop computer, laptop computer, tabletcomputer, a television with one or more processors embedded thereinand/or coupled thereto, smart phone, handheld computer, personal digitalassistant (“PDA”), or any other wired or wireless, processor-drivendevice. In the example embodiment depicted in FIG. 1, the networkdevices 110, 120, 140, and 170 are operated by end-users or consumers,OCR system operators, payment processing system operators, and cardissuer operators, respectively. In certain example embodiments, thevarious operators may have to download an application, activate afeature of an application, and/or otherwise enable an application totake advantage of the features described herein.

The user 101 can use the communication application 112, which may be,for example, a web browser application or a stand-alone application, toview, download, upload, or otherwise access documents or web pages via adistributed network 105. The network 105 includes a wired or wirelesstelecommunication system or device by which network devices (includingdevices 110, 120, 140, and 170) can exchange data. For example, thenetwork 105 can include a local area network (“LAN”), a wide areanetwork (“WAN”), an intranet, an Internet, storage area network (SAN),personal area network (PAN), a metropolitan area network (MAN), awireless local area network (WLAN), a virtual private network (VPN), acellular or other mobile communication network, Bluetooth, NFC, or anycombination thereof or any other appropriate architecture or system thatfacilitates the communication of signals, data, and/or messages.

The user device 110 may employ a communication module 112 to communicatewith the web server 124 of the OCR system 120 or other servers. Thecommunication module 112 may allow devices to communicate viatechnologies other than the network 105. Examples might include acellular network, radio network, or other communication network.

The user computing device 110 may include a digital wallet applicationmodule 111. The digital wallet application module 111 may encompass anyapplication, hardware, software, or process the user device 110 mayemploy to assist the user 101 in completing a purchase. The digitalwallet application module 111 can interact with the communicationapplication 112 or can be embodied as a companion application of thecommunication application 112. As a companion application, the digitalwallet application module 111 executes within the communicationapplication 112. That is, the digital wallet application module 111 maybe an application program embedded in the communication application 112.

The user device 110 may include an optical character recognition (“OCR”)application 115. The OCR application 115 can interact with thecommunication application 112 or be embodied as a companion applicationof the communication application 112 and execute within thecommunication application 112. In an exemplary embodiment, the OCRapplication 115 may additionally or alternatively be embodied as acompanion application of the digital wallet application module 111 andexecute within the digital wallet application module 111. The OCRapplication 115 may employ a software interface that may open in thedigital wallet application 111 or may open in the communicationapplication 112. The interface can allow the user 101 to configure theOCR application 115 and the user account on the offer provider system150.

The OCR application 115 can be used to analyze a card and extractinformation or other data from the card. The OCR system 120 or othersystem that develops the OCR algorithms or other methods may include aset of computer-readable program instructions, for example, usingJavaScript, that enable the OCR system 120 to interact with the OCRapplication 115.

Any of the functions described in the specification as being performedby the OCR application 115 can be performed by the payment processingsystem 140, the OCR system 120, the user computing device 110, amerchant system (not pictured) or any other suitable hardware orsoftware system or application.

The user device 110 includes a data storage unit 113 accessible by theOCR application 115 and the communication application 112. The exemplarydata storage unit 113 can include one or more tangible computer-readablemedia. The data storage unit 113 can be stored on the user device 110 orcan be logically coupled to the user device 110. For example, the datastorage unit 113 can include on-board flash memory and/or one or moreremovable memory cards or removable flash memory.

The user device 110 may include a camera 114. The camera may be anymodule or function of the user computing device 110 that obtains adigital image. The camera 114 may be onboard the user computing device110 or in any manner logically connected to the user computing device110. The camera 114 may be capable of obtaining individual images or avideo scan. Any other suitable image capturing device may be representedby the camera 114.

The payment processing system 140 includes a data storage unit 147accessible by the web server 144. The example data storage unit 147 caninclude one or more tangible computer-readable storage devices. Thepayment processing system 140 is operable to conduct payments between auser 101 and a merchant system (not pictured). The payment processingsystem 140 is further operable to manage a payment account of a user101, maintain a database to store transactions of the merchant systemand the user 101, verify transactions, and other suitable functions.

The user 101 may use a web server 144 on the payment processing system140 to view, register, download, upload, or otherwise access the paymentprocessing system 140 via a website (not illustrated) and acommunication network 105). The user 101 associates one or moreregistered financial card accounts, including bank account debit cards,credit cards, gift cards, loyalty cards, coupons, offers, prepaidoffers, store rewards cards, or other type of financial account that canbe used to make a purchase or redeem value-added services with a paymentaccount of the user 101.

A card issuer, such as a bank or other institution, may be the issuer ofthe financial account being registered. For example, the card issuer maybe a credit card issuer, a debit card issuer, a stored value issuer, afinancial institution providing an account, or any other provider of afinancial account. The payment processing system 140 also may functionas the issuer for the associated financial account. The user's 101registration information is saved in the payment processing system's 140data storage unit 147 and is accessible the by web server 144. The cardissuer employs a card issuer system 170 to issue the cards, manage theuser account, and perform any other suitable functions. The card issuersystem 170 may alternatively issue cards used for identification,access, verification, ticketing, or cards for any suitable purpose. Thecard issuer system 170 may employ a web server 174 to manage the useraccount and issuer cards 102.

The OCR system 120 utilizes an OCR system web server 124 operating asystem that produces, manages, stores, or maintains OCR algorithms,methods, processes, or services. The OCR system web server 124 mayrepresent the computer implemented system that the OCR web system 120employs to provide OCR services to user computing devices 110,merchants, or any suitable part. The OCR system web server 124 cancommunicate with one or more payment processing systems 140, a userdevice 110, or other computing devices via any available technologies.These technologies may include, but would not be limited to, an Internetconnection via the network 105, email, text, instant messaging, or othersuitable communication technologies. The OCR system 120 may include adata storage unit 127 accessible by the web server 124 of the OCR system120. The data storage unit 127 can include one or more tangiblecomputer-readable storage devices.

Any of the functions described in the specification as being performedby the OCR system 120 can be performed by the OCR application 115, theuser computing device 110, or any other suitable hardware or softwaresystem or application.

The user 101 may employ the card 102 when making a transaction, such asa purchase, ticketed entry, loyalty check-in, or other suitabletransaction. The user 101 may obtain the card information for thepurpose of importing the account represented by the card 102 into adigital wallet application module 111 of a computing device 110 or forother digital account purposes. The card 102 is typically a plastic cardcontaining the account information and other data on the card 102. Inmany card 102 embodiments, the customer name, expiration date, and cardnumbers are physically embossed on the card 102. The embossedinformation is visible from both the front and back of the card 102,although the embossed information is typically reversed on the back ofthe card 102.

It will be appreciated that the network connections shown are exemplaryand other means of establishing a communications link between thecomputers and devices can be used. Moreover, those having ordinary skillin the art having the benefit of the present disclosure will appreciatethat the user device 110, OCR system 120, payment processing system 140,and card issuer system 170 illustrated in FIG. 1 can have any of severalother suitable computer system configurations. For example, a userdevice 110 embodied as a mobile phone or handheld computer may notinclude all the components described above.

Example Processes

The example methods illustrated in FIG. 2-7 are described hereinafterwith respect to the components of the example-operating environment 100.The example methods of FIG. 2-7 may also be performed with other systemsand in other environments.

FIG. 2 is a block flow diagram depicting a method 200 for extractingaccount information using a linear classifier, in accordance withcertain example embodiments.

With reference to FIGS. 1 and 2, in block 205, the optical characterrecognition (“OCR”) application 115 on the user device 110 obtains adigital scan of a card 102. The user 101 employs a mobile phone, digitalcamera, or other user computing device 110 to capture an image of thecard 102 associated with the account that the user 101 desires to inputinto the user computing device 110.

The OCR application 115 on the user computing device 110 receives theimage of the card 102. The image may be obtained from the camera moduleof the user computing device 110, such as the camera 114 on a mobilephone. The image may be obtained from a scanner coupled to the usercomputing device 110 or any other suitable digital imaging device. Theimage may be obtained from video captured by the user computing device110. The image may be accessed by the OCR application 115 on the usercomputing device 110 from a storage location on the user computingdevice 110, from a remote storage location, or from any suitablelocation. All sources capable of providing the image will be referred toherein as a “camera” 114.

The functions of the OCR application 115 may be performed by anysuitable module, hardware, software, or application operating on theuser computing device 110. Some, or all, of the functions of the OCRapplication 115 may be performed by a remote server or other computingdevice, such as the server operating in an OCR system 120. For example,a digital wallet application module 111 on the user computing device 110may obtain the images of the card and transmit the images to the OCRsystem 120 for processing. In another example, some of the OCR functionsmay be conducted by the user computing device 110 and some by the OCRsystem 120 or another remote server. Examples provided herein mayindicate that many of the functions are performed by an OCR application115 on the user computing device 110, but some or all of the functionsmay be performed by any suitable computing device.

The image is presented on the user interface of the user computingdevice 110 as a live video image of the card 102. The OCR application115 can isolate and store one or more images from the video feed of thecamera 114. For example, the user 101 may hover the camera 114 functionof a user computing device 110 over the card 102 and observe therepresentation of the card on the user interface of the user computingdevice 110.

In block 210, the OCR application 115 may crop the image to display onlythe desired information from the card 102. For example, if the card 102in the image is a credit card, the OCR application 115 accessesinformation associated with the expected location of the account numberof a credit card. The expected location may be obtained from a databaseof card layouts stored on the user computing device 110 or in anothersuitable location. Credit cards, driver's licenses, loyalty cards, andother cards typically meet an industry standard or a particular issuerstandard for the data locations and the layout of the card. Thestandards may be stored in the OCR application 115 or in a locationaccessible by the OCR application 115. In certain circumstances, thedata locations may be provided by the issuer of the card 102.

An illustration of the card 102 displayed on the user computing deviceis presented in FIG. 8.

FIG. 8 is an illustration of a user computing device 110 displaying animage of a financial card 102, in accordance with certain exampleembodiments. The user computing device 110 is shown as a mobilesmartphone. The user computing device 110 is shown with a display screen805 as a user interface. The card 102 is shown displayed on the usercomputing device 110.

Returning to FIG. 2, in block 215, the OCR application applies a linearclassifier to the image data. The details of the method of block 215 aredescribed in greater detail hereinafter with reference to FIG. 3.

FIG. 3 is a block flow diagram depicting a method 215 for applying alinear classifier to locate digits, in accordance with certain exampleembodiments, as referenced in block 215 of FIG. 2.

In block 305, the OCR application 115 isolates a small window on thecard 102. The window is a section of the image of a configured size thatencompasses the data in the window. For example, the window is a sectionof the image a certain number of pixels by a certain number of pixelswide. The window may be located in an area where a digit is expected tobe located. The window may be slightly larger than the expected digit toallow an entire digit to be encompassed by the window, but not so largeas to encompass more than one digit or more than a limited number ofdigits. Other windows may be utilized that encompass multiple digits.

In block 310, the OCR application 115 applies a linear classifier orother suitable classifier to the window. The OCR application 115 may usea simple linear support vector machine (“SVM”) to distinguish a digitfrom the background. An SVM is a machine learning tool that may utilizealgorithms to analyze data and recognize patterns. The SVM linearclassifier the OCR application 115 applies to the window may use lessprocessing capacity from the user computing device 110 than a moretypical OCR algorithm.

In block 315, the OCR application 115 determines if the window likelyencompasses an entire digit. The classifier determines a confidencelevel that the image in the window is a digit. For example, if thewindow completely encompasses a single digit, the classifier maydetermine that the image is likely to be a digit and provide a highconfidence level. The OCR application 115, or another suitable party,may configure a threshold value of the confidence level for determiningif the window encompasses a digit.

In block 320, if the results of the classifier indicate that theconfidence level is under a threshold and thus does not encompass adigit, then the method 215 follows the “NO” branch of block 320 to block305. If the results of the classifier indicate that the confidence levelis equal to or greater than a threshold and thus does encompass a digit,then the method 215 follows the “YES” branch of block 320 to block 220of FIG. 2.

Following the NO branch of block 320 to block 305, the OCR application115 shifts and/or resizes the window position on the image. The OCRapplication 115 may shift the window location by as little as a pixel onthe image or by any other distance. The OCR application 115 may shiftthe window horizontally, vertically, or diagonally. The size of thewindow may be altered, for example, by enlarging or decreasing a size ofthe window. Any suitable revision to the window may be performed.

The OCR application 115 reapplies the linear classifier to the newwindow and determines if a digit is encompassed by the window asdescribed in blocks 310, 315, and 320. The OCR application 115 may shiftthe window and run linear classifiers until all the locations of alldigits are identified and the confidence levels within those locationsare all over the threshold. Additionally or alternatively, all possiblelocations of the image may be analyzed.

If the digit locations are not identified with a required confidencelevel, the OCR application 115 reports a failure to the user 101 andabandons the attempt. Alternatively, the OCR application 115 may selectthe digit locations with the highest confidence levels despite theconfidence levels being below the threshold.

Following the YES branch of block 320 and returning to FIG. 2, in block220, the OCR application 115 applies an OCR algorithm to the digits inthe selected window locations. The OCR application 115 may apply the OCRalgorithm to all of the windows or digits that are over the confidencelevel, to an individual window or digit, or to any portion of the cardimage. The OCR application 115 performs an OCR algorithm or othercomputer implemented process to determine the card information. In anexample, the OCR application 115 may use an OCR algorithm to analyze thedigit locations in the image of the card 102 to extract the accountnumber of a credit card. The extracted number may be presented to theuser 101 for verification, stored in the OCR application 115,transmitted to a remote computer, stored in a digital wallet applicationmodule 111, or used in any suitable manner. Other information, such asthe user name, expiration date, security code, or any suitableinformation, may also be extracted from the image.

In block 225, the OCR application 115 supplies the extracted data to arequestor, such as a digital wallet application module 111, point ofsale terminal, payment processing system, website, or any suitableapplication or system that the user desires. The extracted data may beused by an application on the user computing device 110. The extracteddata may be transmitted via an Internet connection over the network, viaa near field communication (“NFC”) technology, emailed, texted, ortransmitted in any suitable manner.

FIG. 4 is a block flow diagram depicting a method 400 for an OCR system120 to modify a linear classier to identify wear patterns, in accordancewith certain example embodiments. Embossed digits, printed digits, andother digits on a card often become distressed over a period time. Onesource of distress is wear from being inserted into and out of a wallet,purse, pocket, or other enclosure. The digits can develop a wear patternfrom being forced into an enclosure repeatedly. For example, if a userforces a credit card into a slot in a wallet from the top down, the cardcan develop a wear pattern from the friction of the card on the walletmaterial.

The method 400 of modifying a classifier may be performed by anycomputing device or system, such as by the user computing device 110,the OCR system 120, the payment processing system 140, the card issuersystem 170, or any suitable party. Examples herein are shown with themodifications being performed by the OCR system 120.

In block 405, the OCR system 120 employs a linear classifier. The OCRsystem 120 or any other suitable system may train a classifier toidentify the wear patterns. The classifier may be trained by receivingdata from an operator or another suitable party. The classifier analyzesimages of cards. The classifier may identify digits in a manner asdescribed herein or in any suitable manner.

In block 410, the OCR system 120 classifies digits to train theclassifier. The classifier analyzes images of cards such as card 102.The images may be taken from actual card scans, generated by a computer,designed by an operator, taken from a demonstration group, or obtainedfrom any suitable source. The classifier may identify digits in a manneras described in method 215 of FIG. 3 or in any suitable manner.

In block 415, the OCR system 120 receives corrections for incorrectlyclassified digits. The OCR system 120 operator, a user 101, or anothersuitable party may provide corrections for incorrectly classifieddigits. The operator may view the results of a classification on a userinterface of the OCR system web server 124 and correct classificationerrors. For example, a window may be classified as not containing adigit when a digit was completely contained in the window but worn awaysufficiently that the OCR system 120 does not detect the digit. Theoperator instructs the classifier that the window did contain a digit.The classifier learns that the characteristics indicated by theclassification of the window are to be incorporated in futureclassifications. The classifier learns from the corrections to moreaccurately interpret the image data. For example, a particular wearpattern may wear off a bottom half of a digit. The OCR system 120 willinterpret a window encompassing the digit as not containing an entiredigit, because the bottom half of the digit is missing. However, the tophalf of the supplied digit can be used to interpret the whole digit, andthe classifier can be instructed to recognize the whole digit based onthe top half of the supplied digit. Additionally, the classifier can beinstructed to identify the top half of digits in other windows (or whenanalyzing other cards) and to supply the missing bottom half of thedigit based on the applied wear pattern.

In block 420, the OCR system 120 receives input of wear patterns oncards 102. The OCR system 120 may recognize wear patterns on the cards102. Additionally or alternatively, an operator of the OCR system 120may provide instructions on wear pattern recognition. The operator maytrain the classifier by providing cards 102 with actual wear patterns orsimulated wear patterns.

For example, the digits on a card 102 may have a wear pattern that wascreated by sliding the card 102 into a holder. The classifier may beinstructed to recognize the wear patterns. The classifier may further beinstructed to categorize the wear patterns. For example, a wear patternmay be associated with a category such as “top down wear.”

In an example, a user 101 employs a wallet that has a slotted openinginto which a card 102 slides. If the user 101 places the card 102 intothe wallet by forcing the top of the card 102 into the slot, the carddigits may wear from the top downward. This wear pattern may becategorized as “top down wear.” Other wear pattern categories mightinclude “left side horizontal wear,” “diagonal wear,” “bottom up wear,”or in any suitable category.

In block 425, the OCR system 120 may create transformations to apply toclassification data. For example, the OCR system 120 may learn whatoptical effect each category of wear pattern creates in the card image,and in particular, to specific digits. The OCR system 120 may create atransformation to apply to classification data that allows theclassification data to be rectified, or otherwise revised, to a standarddigit image.

FIG. 5 is a block flow diagram depicting methods for extracting accountinformation using wear patterns, in accordance with certain exampleembodiments.

Blocks 205 and 210 are performed as described in blocks 205 and 210 ofFIG. 2. Block 215 is performed as described in method 215 of FIG. 3.Upon identification of the digits, the method 215 returns to block 220of FIG. 5. Block 220 is performed as described in block 220 of FIG. 2.

In block 525, the OCR application 115 determines a confidence level of aresult of performing the OCR algorithm on the image. The confidencelevel may be determined via any suitable process such as a mathematicaldetermination of the accuracy of the OCR algorithm or any othermanipulation of the data of the results.

In block 530, the OCR application 115 applies one or moretransformations to the linear classification of the data, such as a topdown wear transformation. For example, in a “top down wear”transformation, the OCR application 115 will identify the bottom portionof a number (in other words, a number missing at least a portion of thetop of the number). Then, using the bottom portion of the number, theOCR application 115 can identify the entire number.

In block 535, the OCR application 115 determines the confidence level ofthe result after the transformation. The confidence level may bedetermined by comparing the most likely result from the second mostlikely result. For example, if the top result has a 70% likelihood andthe second result has a 30% likelihood, the confidence level may bepredicted based on the difference between the two likelihoods. The OCRapplication 115 may determine the confidence level improves after eachof the transformations. If the confidence level improves after atransformation, then the OCR application 115 determines that the card inthe image has a wear pattern associated with the associated wearpattern.

In block 540, the OCR application 115 selects the top performing wearpattern transformation result. The top performing wear pattern may bethe wear pattern that produces the highest confidence levels in theresults of the transformation. For example, the top down wear patterntransformation may produce an 80% confidence level and the bottom upwear pattern transformation may produce a 60% confidence level. The topdown wear pattern transformation will be the top performing result. TheOCR application 115 may additionally or alternatively combine wearpattern transformations in any combination that provides a suitableresult. For example, if a user randomly places card in his card holder,then that user's cards may exhibit multiple, different wear patterns.

In block 545, after determining the wear pattern associated with thecard 102 of the user 101, the OCR application 115 applies the wearpattern to all subsequent card images. That is, when the user 101applies a classification and an OCR algorithm to future card images, theOCR application 115 applies the determined wear pattern to theclassification. The OCR application 115 assumes that the user 101creates a similar wear pattern on all cards 102 utilized by the user101. For example, the user 101 may store all cards in a similar mannerin a wallet or other card holder. Thus, a similar wear pattern may becreated on all cards 102 of the user 101. The OCR application 115 usesthe predicted wear pattern to improve the accuracy and the speed of theOCR application 115. By predicting the wear pattern of the user 101 andapplying the associated wear pattern to the user data at the beginningof the process, the OCR application 115 may more accurately interpretthe data. The OCR application 115 may additionally conduct the OCRprocess faster by using the transformed data.

FIG. 6 is a block flow diagram depicting methods for extracting accountinformation using linear and non-linear classifiers, in accordance withcertain example embodiments.

Blocks 205 and 210 are performed as described in blocks 205 and 210 ofFIG. 2.

In block 615 the OCR application 115 applies a linear transformation anda nonlinear transformation method to the card image. Lineartransformation methods may include techniques such as simple linearsupport vector machines (“SVM”), principal components analysis (“PCA”),and linear discriminant analysis (“LDA”). The classification method maybe selected to reduce the dimensionality of the input features whileminimizing the impact on the accuracy of the results. The OCRapplication 115 may additionally or alternatively select a method thatrequires the least processing capacity from the user network device. Anexample of nonlinear transformation methods includes Random FourierFeature Mapping (“RFFM”). The nonlinear transformation method improvesthe accuracy of the data. By first reducing the dimensionality of thedata with the linear classification methods and then increasing thedimensionality with the nonlinear transformation methods, the OCRapplication achieves higher accuracy with reduced processing costs.

The OCR application 115 generates more accurate results with lessprocessing requirements by combining linear and nonlinear transformationmethods. The OCR application 115 applies a linear transformation to theimage data to reduce the dimensionality and then applies the nonlinearclassification to increase the accuracy of the results. Dimensionalityreduction is the process of reducing the number of random variablesunder consideration. The main linear technique for dimensionalityreduction, principal component analysis, performs a linear mapping ofthe data to a lower dimensional space in such a way that the variance ofthe data in the low-dimensional representation is maximized. Thecovariance matrix of the data is constructed and the eigenvectors onthis matrix are computed. The eigenvectors that correspond to thelargest eigenvalues (the principal components) can now be used toreconstruct a large fraction of the variance of the original data. Theoriginal space (with dimension of the number of points) has been reduced(with data loss, but hopefully retaining the most important variance) tothe space spanned by a few eigenvectors.

By first reducing the dimensionality of the data with the lineartransformation methods and then increasing the dimensionality with thenonlinear transformation methods, the OCR application 115 achieveshigher accuracy with reduced processing costs.

In block 617, the OCR application 115 applies a classification method tothe card image. The classification method may be linear or nonlinear, oremploy any available classification method.

In block 220, the OCR application 115 applies an OCR algorithm to theresulting image as described in block 220 of FIG. 2.

In block 225, the OCR application 115 supplies the card information asdescribed in block 225 of FIG. 2.

FIG. 7 is a block flow diagram depicting methods for extracting accountinformation using card models, in accordance with certain exampleembodiments.

Blocks 205 and 210 are performed as described in blocks 205 and 210 ofFIG. 2.

In block 215, the OCR application 115 applies a linear classifier to theimage as described in method 215 of FIG. 3. The OCR application 115determines the potential digit locations. That is, the OCR application115 can score each potential digit window and select the windows mostlikely to contain a digit. The method 215 proceeds to block 716 of FIG.7.

In block 716, the OCR application 115 identifies lines on the card imageby fitting lines to the likely digits. That is the OCR application 115may apply lines to the image that are fitted to the top or bottom of aseries of digits. For example, the digits comprising the useridentification number of a credit card are typically printed or embossedin a line from left to right across the face of the card 102. The OCRapplication 115 recognizes that the potential digits are arranged in aline or a series of lines. The lines most likely to be lines of digitsmay be stored. For example, the OCR application 115 may store the topscoring 3 or 4 lines.

In block 718, the OCR application 115 fits card models to the image. TheOCR application 115 maintains a series of card models in a database orother storage location. The card models indicate the manner in which thedigits are displayed on the card 102. For example, certain card issuersdisplay the account numbers in a particular spacing configuration and ata certain location on the card 102. A certain card issuer system 170 maydisplay the digits of the account number in a continuous string ofdigits without spaces. Another card issuer system 170 may display thedigits of the account number in groups of 4 digits divided by spaces.Another card issuer system 170 may display the digits of the accountnumber in groups of 4 digits with a double space in the center of thedigits. Any suitable digit grouping may be identified and stored in thedatabase.

The card models to use in the comparison may be selected based onknowledge of the card types associated with the user 101. For example,if the user 101 typically employs cards from a particular card issuersystem 170, then card models associated with the card issuer system 170may be used in the comparison. In another example, the OCR application115 may recognize the card type in the image and employ associated cardmodels. Any suitable card model selection criteria may be employed.

The OCR application 115 fits the card models to the image. The OCRapplication 115 may fit the model along the lines stored from the image.That is, the OCR application 115 fits the model along each of the linesthat were determined to be likely digit location lines. In anotherexample, the model is fit to every location on the card image. The modelmay be applied in a location against the left edge of the card and thenshifted one pixel at a time horizontally or vertically to find the bestfit. Any other method of fitting the model to the image may be employed.

In block 220, the OCR application 115 applies an OCR algorithm to thepotential digit locations determined by the lines, the windows, and themodels. The OCR algorithm application is substantially similar to block220 of FIG. 2.

In block 725, the OCR application 115 shifts the model along thedetermined lines on the card image. The OCR application 115 determinesthe best digit recognition scores from application of the OCR algorithmto the different model locations.

In block 730, the OCR application 115 determines the model that createsthe best digit recognition scores. For example, a model that has thedigits of the account number in groups of 4 digits may generate the bestresults. The OCR application 115 may predict a credit card issuer thatis associated with the account number groupings. The OCR application 115uses the knowledge of the credit card issuer to predict other datalocations on the image or for any suitable validation, prediction, orverification purposes.

The OCR application 115 selects the best digits located by the lines,the windows, and the models. The OCR application 115 verifies theselected digits as the most likely digits. The verification may bepresented to the user 101.

In block 735, the OCR application 115 supplies the relevant cardinformation to a requestor, such as a digital wallet application module111, point of sale terminal, payment processing system, website, or anysuitable application or system that the user desires. The extracted datamay be used by an application on the user computing device 110. Theextracted data may be transmitted via an Internet connection over thenetwork, via a near field communication (“NFC”) technology, emailed,texted, or transmitted in any suitable manner.

Other Example Embodiments

FIG. 9 depicts a computing machine 2000 and a module 2050 in accordancewith certain example embodiments. The computing machine 2000 maycorrespond to any of the various computers, servers, mobile devices,embedded systems, or computing systems presented herein. The module 2050may comprise one or more hardware or software elements configured tofacilitate the computing machine 2000 in performing the various methodsand processing functions presented herein. The computing machine 2000may include various internal or attached components such as a processor2010, system bus 2020, system memory 2030, storage media 2040,input/output interface 2060, and a network interface 2070 forcommunicating with a network 2080.

The computing machine 2000 may be implemented as a conventional computersystem, an embedded controller, a laptop, a server, a mobile device, asmartphone, a set-top box, a kiosk, a vehicular information system, onemore processors associated with a television, a customized machine, anyother hardware platform, or any combination or multiplicity thereof. Thecomputing machine 2000 may be a distributed system configured tofunction using multiple computing machines interconnected via a datanetwork or bus system.

The processor 2010 may be configured to execute code or instructions toperform the operations and functionality described herein, managerequest flow and address mappings, and to perform calculations andgenerate commands. The processor 2010 may be configured to monitor andcontrol the operation of the components in the computing machine 2000.The processor 2010 may be a general purpose processor, a processor core,a multiprocessor, a reconfigurable processor, a microcontroller, adigital signal processor (“DSP”), an application specific integratedcircuit (“ASIC”), a graphics processing unit (“GPU”), a fieldprogrammable gate array (“FPGA”), a programmable logic device (“PLD”), acontroller, a state machine, gated logic, discrete hardware components,any other processing unit, or any combination or multiplicity thereof.The processor 2010 may be a single processing unit, multiple processingunits, a single processing core, multiple processing cores, specialpurpose processing cores, co-processors, or any combination thereof.According to certain example embodiments, the processor 2010 along withother components of the computing machine 2000 may be a virtualizedcomputing machine executing within one or more other computing machines

The system memory 2030 may include non-volatile memories such asread-only memory (“ROM”), programmable read-only memory (“PROM”),erasable programmable read-only memory (“EPROM”), flash memory, or anyother device capable of storing program instructions or data with orwithout applied power. The system memory 2030 may also include volatilememories such as random access memory (“RAM”), static random accessmemory (“SRAM”), dynamic random access memory (“DRAM”), and synchronousdynamic random access memory (“SDRAM”). Other types of RAM also may beused to implement the system memory 2030. The system memory 2030 may beimplemented using a single memory module or multiple memory modules.While the system memory 2030 is depicted as being part of the computingmachine 2000, one skilled in the art will recognize that the systemmemory 2030 may be separate from the computing machine 2000 withoutdeparting from the scope of the subject technology. It should also beappreciated that the system memory 2030 may include, or operate inconjunction with, a non-volatile storage device such as the storagemedia 2040.

The storage media 2040 may include a hard disk, a floppy disk, a compactdisc read only memory (“CD-ROM”), a digital versatile disc (“DVD”), aBlu-ray disc, a magnetic tape, a flash memory, other non-volatile memorydevice, a solid state drive (“SSD”), any magnetic storage device, anyoptical storage device, any electrical storage device, any semiconductorstorage device, any physical-based storage device, any other datastorage device, or any combination or multiplicity thereof. The storagemedia 2040 may store one or more operating systems, application programsand program modules such as module 2050, data, or any other information.The storage media 2040 may be part of, or connected to, the computingmachine 2000. The storage media 2040 may also be part of one or moreother computing machines that are in communication with the computingmachine 2000 such as servers, database servers, cloud storage, networkattached storage, and so forth.

The module 2050 may comprise one or more hardware or software elementsconfigured to facilitate the computing machine 2000 with performing thevarious methods and processing functions presented herein. The module2050 may include one or more sequences of instructions stored assoftware or firmware in association with the system memory 2030, thestorage media 2040, or both. The storage media 2040 may thereforerepresent examples of machine or computer readable media on whichinstructions or code may be stored for execution by the processor 2010.Machine or computer readable media may generally refer to any medium ormedia used to provide instructions to the processor 2010. Such machineor computer readable media associated with the module 2050 may comprisea computer software product. It should be appreciated that a computersoftware product comprising the module 2050 may also be associated withone or more processes or methods for delivering the module 2050 to thecomputing machine 2000 via the network 2080, any signal-bearing medium,or any other communication or delivery technology. The module 2050 mayalso comprise hardware circuits or information for configuring hardwarecircuits such as microcode or configuration information for an FPGA orother PLD.

The input/output (“I/O”) interface 2060 may be configured to couple toone or more external devices, to receive data from the one or moreexternal devices, and to send data to the one or more external devices.Such external devices along with the various internal devices may alsobe known as peripheral devices. The I/O interface 2060 may include bothelectrical and physical connections for operably coupling the variousperipheral devices to the computing machine 2000 or the processor 2010.The I/O interface 2060 may be configured to communicate data, addresses,and control signals between the peripheral devices, the computingmachine 2000, or the processor 2010. The I/O interface 2060 may beconfigured to implement any standard interface, such as small computersystem interface (“SCSI”), serial-attached SCSI (“SAS”), fiber channel,peripheral component interconnect (“PCI”), PCI express (PCIe), serialbus, parallel bus, advanced technology attached (“ATA”), serial ATA(“SATA”), universal serial bus (“USB”), Thunderbolt, FireWire, variousvideo buses, and the like. The I/O interface 2060 may be configured toimplement only one interface or bus technology. Alternatively, the I/Ointerface 2060 may be configured to implement multiple interfaces or bustechnologies. The I/O interface 2060 may be configured as part of, allof, or to operate in conjunction with, the system bus 2020. The I/Ointerface 2060 may include one or more buffers for bufferingtransmissions between one or more external devices, internal devices,the computing machine 2000, or the processor 2010.

The I/O interface 2060 may couple the computing machine 2000 to variousinput devices including mice, touch-screens, scanners, electronicdigitizers, sensors, receivers, touchpads, trackballs, cameras,microphones, keyboards, any other pointing devices, or any combinationsthereof. The I/O interface 2060 may couple the computing machine 2000 tovarious output devices including video displays, speakers, printers,projectors, tactile feedback devices, automation control, roboticcomponents, actuators, motors, fans, solenoids, valves, pumps,transmitters, signal emitters, lights, and so forth.

The computing machine 2000 may operate in a networked environment usinglogical connections through the network interface 2070 to one or moreother systems or computing machines across the network 2080. The network2080 may include wide area networks (WAN), local area networks (LAN),intranets, the Internet, wireless access networks, wired networks,mobile networks, telephone networks, optical networks, or combinationsthereof. The network 2080 may be packet switched, circuit switched, ofany topology, and may use any communication protocol. Communicationlinks within the network 2080 may involve various digital or an analogcommunication media such as fiber optic cables, free-space optics,waveguides, electrical conductors, wireless links, antennas,radio-frequency communications, and so forth.

The processor 2010 may be connected to the other elements of thecomputing machine 2000 or the various peripherals discussed hereinthrough the system bus 2020. It should be appreciated that the systembus 2020 may be within the processor 2010, outside the processor 2010,or both. According to some embodiments, any of the processor 2010, theother elements of the computing machine 2000, or the various peripheralsdiscussed herein may be integrated into a single device such as a systemon chip (“SOC”), system on package (“SOP”), or ASIC device.

In situations in which the systems discussed here collect personalinformation about users, or may make use of personal information, theusers may be provided with a opportunity to control whether programs orfeatures collect user information (e.g., information about a user'ssocial network, social actions or activities, profession, a user'spreferences, or a user's current location), or to control whether and/orhow to receive content from the content server that may be more relevantto the user. In addition, certain data may be treated in one or moreways before it is stored or used, so that personally identifiableinformation is removed. For example, a user's identity may be treated sothat no personally identifiable information can be determined for theuser, or a user's geographic location may be generalized where locationinformation is obtained (such as to a city, ZIP code, or state level),so that a particular location of a user cannot be determined. Thus, theuser may have control over how information is collected about the userand used by a content server.

Embodiments may comprise a computer program that embodies the functionsdescribed and illustrated herein, wherein the computer program isimplemented in a computer system that comprises instructions stored in amachine-readable medium and a processor that executes the instructions.However, it should be apparent that there could be many different waysof implementing embodiments in computer programming, and the embodimentsshould not be construed as limited to any one set of computer programinstructions. Further, a skilled programmer would be able to write sucha computer program to implement an embodiment of the disclosedembodiments based on the appended flow charts and associated descriptionin the application text. Therefore, disclosure of a particular set ofprogram code instructions is not considered necessary for an adequateunderstanding of how to make and use embodiments. Further, those skilledin the art will appreciate that one or more aspects of embodimentsdescribed herein may be performed by hardware, software, or acombination thereof, as may be embodied in one or more computingsystems. Moreover, any reference to an act being performed by a computershould not be construed as being performed by a single computer as morethan one computer may perform the act.

The example embodiments described herein can be used with computerhardware and software that perform the methods and processing functionsdescribed herein. The systems, methods, and procedures described hereincan be embodied in a programmable computer, computer-executablesoftware, or digital circuitry. The software can be stored oncomputer-readable media. For example, computer-readable media caninclude a floppy disk, RAM, ROM, hard disk, removable media, flashmemory, memory stick, optical media, magneto-optical media, CD-ROM, etc.Digital circuitry can include integrated circuits, gate arrays, buildingblock logic, field programmable gate arrays (FPGA), etc.

The example systems, methods, and acts described in the embodimentspresented previously are illustrative, and, in alternative embodiments,certain acts can be performed in a different order, in parallel with oneanother, omitted entirely, and/or combined between different exampleembodiments, and/or certain additional acts can be performed, withoutdeparting from the scope and spirit of various embodiments. Accordingly,such alternative embodiments are included in the invention claimedherein.

Although specific embodiments have been described above in detail, thedescription is merely for purposes of illustration. It should beappreciated, therefore, that many aspects described above are notintended as required or essential elements unless explicitly statedotherwise. Modifications of, and equivalent components or actscorresponding to, the disclosed aspects of the example embodiments, inaddition to those described above, can be made by a person of ordinaryskill in the art, having the benefit of the present disclosure, withoutdeparting from the spirit and scope of embodiments defined in thefollowing claims, the scope of which is to be accorded the broadestinterpretation so as to encompass such modifications and equivalentstructures.

What is claimed is:
 1. A computer-implemented method for extracting cardinformation, comprising: identifying, by the one or more computingdevices, a first area of the image, the first area being selected as apotential location of one or more digits on the image; performing, bythe one or more computing devices, a classification algorithm on dataencompassed by the first area; comparing, by the one or more computingdevices, the image to one or more card models associated with a user,the one or more models comprising digit distribution patterns of datadisplayed on the image; and performing, by the one or more computingdevices, an optical character recognition algorithm on areas of the cardthat are anticipated by the one or more computing devices as comprisingdigits based on the application of the card models and the identifiedlines.
 2. The method of claim 1, further comprising selecting, by theone or more computing devices, the one or more card models based atleast in part on stored user data indicating card types that areassociated with the user.
 3. The method of claim 1, further comprisingcomparing, by the one or more computing devices, the model associatedwith the authenticated result to a database of card types to determine acard type of the card in the image.
 4. The method of claim 1, furthercomprising identifying, by the one or more computing devices, one ormore lines of potential digits on the image based on the results of theapplication of the classification.
 5. The method of claim 4, wherein thelines are identified by analyzing, by the one or more computing devices,a position of the identified digits relative to each other and fittinglines to the positions.
 6. The method of claim 1, wherein the cardmodels represent digit distribution patterns for known card issuers. 7.The method of claim 1, wherein the card is a credit card, a debit card,an identification card, a loyalty card, an access card, or a storedvalue card.
 8. A computer program product, comprising: a non-transitorycomputer-readable storage device having computer-executable programinstructions embodied thereon that when executed by a computer cause thecomputer to extract card information, comprising: computer-executableprogram instructions to identify a first area of the image, the firstarea being selected as a potential location of one or more digits on theimage; computer-executable program instructions to perform aclassification algorithm on data encompassed by the first area;computer-executable program instructions to compare the image to one ormore card models associated with a user, the one or more modelscomprising digit distribution patterns of data displayed on the image;and computer-executable program instructions to perform an opticalcharacter recognition algorithm on areas of the card that areanticipated by the one or more computing devices as comprising digitsbased on the application of the card models and the identified lines. 9.The computer program product of claim 8, the computer-executable programinstructions further comprising selecting the one or more card modelsbased at least in part on stored user data indicating card types thatare associated with the user.
 10. The computer program product of claim8, the computer-executable program instructions further comprisingcomparing the model associated with the authenticated result to adatabase of card types to determine a card type of the card in theimage.
 11. The computer program product of claim 8, wherein theclassification algorithm is a support vector machine.
 12. The computerprogram product of claim 8, wherein the card models represent digitdistribution patterns for known card issuers.
 13. The computer programproduct of claim 8, wherein the card is a credit card, a debit card, anidentification card, a loyalty card, an access card, or a stored valuecard.
 14. The computer program product of claim 8, thecomputer-executable program instructions further comprising identifyingone or more lines of potential digits on the image based on the resultsof the application of the classification.
 15. The computer programproduct of claim 14, wherein the lines are identified by analyzing aposition of the identified digits relative to each other and fittinglines to the positions.
 16. A system for extracting financial cardinformation with relaxed alignment, the system comprising: a storagedevice; a processor communicatively coupled to the storage device,wherein the processor executes application code instructions that arestored in the storage device to cause the system to: identify a firstarea of the image, the first area being selected as a potential locationof one or more digits on the image; perform a classification algorithmon data encompassed by the first area; compare the image to one or morecard models, the one or more models comprising digit distributionpatterns of data displayed on the image; and perform an opticalcharacter recognition algorithm on areas of the card that areanticipated by the one or more computing devices as comprising digitsbased on the application of the card models and the identified lines.17. The system of claim 16, the processor executing further applicationcode instructions that are stored in the storage device and that causethe system to select the one or more card models based at least in parton stored user data indicating card types that are associated with theuser.
 18. The system of claim 16, the processor executing furtherapplication code instructions that are stored in the storage device andthat cause the system to compare the model associated with theauthenticated result to a database of card types to determine a cardtype of the card in the image.
 19. The system of claim 16, wherein theclassification algorithm is a support vector machine.
 20. The system ofclaim 16, wherein the card models represent digit distribution patternsfor known card issuers.
 21. The system of claim 16, wherein the card isa credit card, a debit card, an identification card, a loyalty card, anaccess card, or a stored value card.
 22. The system of claim 16, theprocessor executing further application code instructions that arestored in the storage device and that cause the system to identify oneor more lines of potential digits on the image based on the results ofthe application of the classification.
 23. The system of claim 22,wherein the lines are identified by analyzing, by the one or morecomputing devices, a position of the identified digits relative to eachother and fitting lines to the positions.